This study introduces an innovative diagnostic approach for identifying gate-valve failures in water distribution systems. By implementing high-frequency pressure sensors upstream and downstream of the gate valves, we obtained detailed pressure data that are pivotal for fault diagnosis. We explored three distinct machine-learning algorithms and two data-handling techniques to ensure optimal performance in real-world applications. In our methodology, supervised learning algorithms are used to analyze pressure differentials and predict valve behavior. We rigorously tested these algorithms using both raw and feature-engineered data, and the results indicated the effectiveness of the Gaussian-naïve Bayes model with six extracted features. This approach enhances the precision and reliability of diagnostics in water distribution networks.

  • An innovative diagnostic approach is introduced to identify gate-valve failures in water distribution systems.

  • Three distinct machine-learning algorithms and two data-handling techniques were explored.

  • Supervised learning algorithms were used to analyze pressure differentials and predict valve behavior.

  • The selected approach enhances the precision and reliability of diagnostics in water distribution networks.

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